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IOSR Journal of Economics and Finance (IOSR-JEF)
e- ISSN: 2321-5933, p-ISSN: 2321-5925.Volume 9, Issue 2 Ver.1 (Mar-Apr .2018), PP 28-33
www.iosrjournals.org
DOI: 10.9790/5933-0902012833 www.iosrjournals.org 28 | Page
Effect of Climate Change on Maize Productivity in Kenya:
A Vector Error Correction Model
Wilfrey Vuhya Siahi1, Harrison Kimutai Yego2,Mathew Kipkoech Bartilol3
1(Department Of Agricultural Economics And Resource Management, Moi University, Kenya)
2(Department Of Agricultural Economics And Resource Management, Moi University, Kenya)
3(Department Of Agricultural Economics And Resource Management, Moi University, Kenya)
Corresponding auther:Wilfreyvuhya Siahi
Abstract: As Much As Agriculture Is The Major Catalyst For Any Nation’s Economy, It Relies Heavily On
Rainfall, Which Has Changed In Terms Of Rainfall Patterns. SSA Countries (97%) Rely On Rain Fed
Agriculture. These Countries’ Populations Remain Vulnerable To Climate Change. This Study Sought To
Analyze The Effect Of Climate Change Of Maize Productivity VECM. A Production Function Was Used Where
The Most Commonly Used Weather Indicators, Which Are Precipitation; Temperature Averages And CO2
Concentration Were Incorporated. Unit Roots Were Done Using The Augmented Dickey-Fuller Test And
Cointegration By Johansen Cointegration Test, Where Variables Were Found To Be Cointegrated.
Temperature, Temperature Squared, CO2 And CO2 Squared Were Found To Be Statistically Significant. From
The ECM Results, Rainfall Squared, Temperature And Carbon Dioxide Squared Had A Positive (Direct) And
Significant Effect On Maize Output Respectively (P-Value 0.000, 0.011, 0,034< 0,05). Rainfall, Temperature
Squared And Carbon Dioxide Had A Negative (Indirect) Significant Effect On Maize Output Respectively (P-
Value 0.000, 0.014, 0.002< 0.05). It Is Recommended That The Government To Seriously Use The Metrological
Departments To Monitor The Key Indicators Of The Climate So As To Advice The Stakeholders Accordingly.
Key Words: Error Correction Models (ECM, Cointegration
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Date of Submission: 05-03-2018 Date of acceptance: 19-03-2018
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I. Introduction
Agriculture Has The Potential To Be The Industrial And Economic Catalyst From Which A Nation‟s
Economic Development Can Take Off(Karshenas, 2001); (Hwa, 1989). The Sector Remains As One Of The
Main Source Of Livelihoods For The Rural Poor In Sub-Saharan African Countries. According To The Works
Of Alvaro 2009, Rain Fed Agriculture Dominates Agricultural Production In SSA Countries Covering About
97% Of The Total Cropland And Exposes Agricultural Production To High Rainfall Variability. Africa Must
Provide For An Additional 3.5 Billion People In The Next 50 Years (Mellor, 2014). This Is Made More
Difficult As Climate Change Scenarios In The Region Show That Agricultural Production Will Largely Be
Negatively Affected And Thus Impeding The Ability Of The Region In Achieving The Essential Gains For
Future Food Security (Cassman, Grassini, & Van Wart, 2010)
Agriculture Is The Major Sector Of Sustainable Development In Africa Given Its Contribution To The
Economic Growth And Employment. It Employs Over 70 % Of The Labor Force In Africa (Palacios-Lopez,
Christiaensen, &Kilic, 2017) And Contributes Significantly To GDP. An Effective Agricultural Policy Must
Take Into Account The Effects Of Climate Change In Order To Meet The Commitments Of Maputo In 2003 To
Make Agriculture The Engine Of Agricultural Growth In Africa. The Sector Is One Of The Major Economic
Sectors Significantly Affected By Climate Variability And Change Globally(Cassman Et Al., 2010). (Brown,
Gorski, &Lazaridis, 2014) Note That Climate Change And Climate Variability Are Projected To Contribute To
Increased Drought Episodes, Food Insecurity, Irreversible Decline In Herd Sizes, And Deepening Poverty.
Climate Change Therefore Presents A Challenge For Researchers Attempting To Quantify Its Local Impact Due
To The Universal Scale In Global Scale Of Likely Impacts And The Multiplicity Of Agricultural Systems.
The Fifth Assessment Report Of The United Nations Inter-Governmental Panel On Climate Change (IPCC –
2014) Concluded That “Beyond Reasonable Doubt, The Earth‟s Climate Is Warming” (IPCC, 2014). The Report
Went On To Note That Climate Change Will Have Widespread Impacts On The African Society And Africans‟
Interaction With The Natural Environment.Earlier On In 2007, The IPCC Had Alerted Global Policymakers
That Communities With The Least Resources Have The Lowest Ability To Adapt To Climate-Related
Consequences And Are, Therefore, Often Most Vulnerable To Climatic Changes.
Effect of Climate Change on Maize Productivity in Kenya:A Vector Error Correction Model
DOI: 10.9790/5933-0902012833 www.iosrjournals.org 29 | Page
II. Literature Review
Theoretical Literature
The Effects Of Climate Change Were Evaluated By Several Scholars With Consideration Given Only
To The Changes In The Production Of Specific Crops (Principally Maize, Rice, Cotton And Soybean), Using
The So-Called „Crop Simulation Models‟. According To The Works Of (Josef, 2003) These Models Restrict
The Analysis To Crop Physiology, And Simulate And Compare Crop Productivity For Different Climatic
Conditions. Others Scholars Estimated The Sensitivity Of Yields To Climate Using Empirical Yield Models
That Apply The Production–Function Approach ((Terjung, Hayes, O‟Rourke, &Todhunter, 1984)). The Basic
Idea Of This Approach Is That The Growth Of Agricultural Production Depends On Soil-Related And Climatic
Variables That Are Implemented As Explanatory Variables In The Model For Estimating The Production
Function. Changes In Climate Scenarios Are Usually Simulated Using The General Circulation Model (GCM)
(Liang, Kunkel, Meehl, Jones, &Wang, 2008)(Colman &Mcavaney, 1995).
In The Production Function Approach, The Economic Dimension Is Of Secondary Importance And Is
Considered In A Partial And Simplified Manner(Alboghdady&El-Hendawy, 2016), Even If These Models
Produce Important Information For Larger Model Frameworks That Consider Economy, Later Discussed. Some
Studies Explicitly Assess The Economic Impact Of Climate Change Through The Estimation Of The Economic
Production Function (Assunção&Chein, 2016). However, Other Research Evaluates The Economic Effects Of
Climate Change By Implementing The Results Of Agronomic Analyses Or Of Empirical Yields Models In
Mathematical-Programming Models (Bernués, Rodríguez-Ortega, Ripoll-Bosch, &Alfnes, 2014).
The Main Weakness Of The Production–Function Model Is That It Is Crop And Site Specific. It
Endorses The So-Called „Dumb-Farmer‟ Hypothesis, Which Excludes From Analysis The Plausible Adoption
By Farmers Of Strategies For Coping With The Effects Of Climate Change, For Example, Strategies That
Replace Crops That Are Most Sensitive With Others That Are Less So (Webb, Rosenzweig, &Levine, 1993).
Empirical Literature
Kumar Et Al (2014) Using Panel Regression Analysis For Thirteen States In India Examined The
Effects Of Climatic And Non-Climatic Factors On Sustenance Grain Profitability In India. The Study Covered
1980-2009. Their Findings The Efficiency Of Rice And Maize Crops Are Adversely Impacted By Increment In
Genuine Normal Most Extreme Temperature. On The Other Hand, Actual Minimum Temperature Has A
Negative And Significant Influence On The Productivity Of Wheat, Barley And Grain.
The World Bank Identifies Five Main Factors Through Which Climate Change Affects The Efficiency
Of Agricultural Yields: Changes In Precipitation, Temperature, Carbon Dioxide (CO2), Treatment, Atmosphere
Fluctuation, And Surface Water Overflow. Expanded Atmosphere Changeability And Dry Spells Will Influence
Animal Generation Also. Yield Creation Is Specifically Impacted By Precipitation And Temperature.
Precipitation Decides The Accessibility Of Freshwater And The Level Of Soil Dampness, Which Are Basic
Contributions For Edit Development. In View Of An Econometric Investigation, Reilly Et Al. (2003) Found
That Higher Precipitation Prompts A Decrease In Yield Inconstancy. In This Way, Higher Precipitation Will
Diminish The Yield Hole Between Rain Nourished And Watered Farming, Yet It Might Likewise Have A
Negative Effect If Extraordinary Precipitation Causes Flooding (Falloon&Betts, 2010).
(Sonneveld, 2011), Found That Under Average Climate Change Conditions In The Ouémé River Basin
In Benin, The Present Low Yields Are Not Decreased, Given That Trimming Designs Are Balanced, While Cost
Increments Halfway Make Up For The Staying Unfavorable Impacts On Rancher Salary. Thus, With No
Approach Mediation, Cultivate Earnings Remain Moderately Steady, However At Low Levels And With
Expanded Event Of Yield Disappointments After Extraordinary Dry Seasons. Their Situation Reenactments
Demonstrate That There Are Likewise Useful Perspectives That Can With Satisfactory Mediations Even
Transform Misfortunes Into Picks Up.
Using The Production Function Approach (Awad, Griffiths, &Turpie, 2002)Analyse The Monetary
Effect Of Environmental Change In South Africa. Their Examination Tends To Impacts On Characteristic,
Agrarian, Man-Made And Human Capital. They Foresee That The Effect Of Environmental Change On
Rangelands Will Be Sure, With The Treatment Effect Of CO2 Exceeding The Negative Impacts Of Diminished
Precipitation. In Any Case, They Discover That The Effect Of Environmental Change On Maize Creation Will
Be Negative Both 'With' And 'Without' CO2 Preparation. (Islam Et Al., 2016) Used The Same Approach To
Analyse The Impact Of Climate In Sub Saharan Africa. She Related Respects Standard Climate Factors, For
Example, Temperature And Precipitation, And Modern Climate Measures, For Example, Evapotranspiration
And The Institutionalized Precipitation File. (Islam Et Al., 2016), Shows That Temperature And Precipitation
Are Important Determinants Of The Crop Yields In Sub Saharan Africa.
(Seo, Mendelsohn, &Munasinghe, 2005) Additionally Utilized The Ricardian Way To Deal With
Measure The Effect Of Environmental Change On Sri Lankan Horticulture, Concentrating On Four Noteworthy
Products. The Creators Found That An Earth-Wide Temperature Boost Is Required To Be Hurtful To Sri Lanka
Effect of Climate Change on Maize Productivity in Kenya:A Vector Error Correction Model
DOI: 10.9790/5933-0902012833 www.iosrjournals.org 30 | Page
Yet Increments In Precipitation Will Be Advantageous. They Additionally Find That With Warming, The
Officially Dry Districts Are Required To Lose Huge Extents Of Their Present Horticulture, Yet The Cooler
Areas Are Anticipated To Continue As Before Or Increment Their Yield. They Reasoned That Environmental
Change Harms Could Be Broad In Tropical Creating Nations However Will Rely Upon Genuine Atmosphere
Situations.
(Ngondjeb, 2013) In An Analysis Of The Impact Of Climate On Agriculture In Cameroon Found That
Increased Precipitation Is Beneficial For Crop Production And That Farm Level Adaptations Are Associated
With Increased Farm Returns.
III. Methodology
Data The Area Under Study Is Kenya With The Study Using Time Series Data Spanning From 1961 To
2015. The Data Were Sourced From Food And Agriculture Organisation Database (FAOSTAT) And The
African Climate Change Portal.
Theoretical Model
In Order To Determine The Effect Of Climate Change On Maize Production In Kenya, We Specify A
Production Function Approach (Awad Et Al., 2002). The Model Includes The Most Commonly Used Weather
Indicators, Which Are Precipitation, Temperature Averages And CO2 Concentration. The Production Model
Can Be Specified As Follows:
Qt = Σ(Z)……………………. (1)
Where Z Is A Set Of Climatic Variables: Rainfall, Temperature And Precipitation. The Standard Production
Function Equation Relies On A Quadratic Formulation Of Climate:
Ln(Qt) = Α+Α1lnz + Α2lnz2 + Μ…………………. (2)
Where Μ Is The Error Term. Both The Linear And Quadratic Terms For The Climatic Variables Are
Introduced.
Climate Change Simulation
After Estimating The Impact Of Climate Change On Maize Production, The Study Examines How
Future Changes In Climate Will Affect Maize Outputs. The Study Uses The Uniform Climate Change
Scenarios. Under This Scenario, The Impact Of Climate Change On Maize Production Is Analysed By Using
Uniformly Changing Temperature And Precipitation. The Study Assumed Uniform Change Scenarios Of An
Increase In Temperature By 2oc And 5oc And A Decrease In Precipitation By 5% And 10%.
Cointegration And Unit Root Testing
The Co-Integration Analysis Involves Unit Roots Test Performed On Both Level And First Difference
To Determine Whether The Individual Input Series Are Stationary And Exhibit Similar Statistical Properties. It
Must Be Noticed That Relapsing Non-Stationary Time Arrangement Information Over Non-Stationary Time
Arrangement Information Gives A Deceptive Or Babble Relapse. To Amend For This, A Unit Root Test Is
Performed. Augmented Dickey Fuller (ADF) Test Was Utilized To Test For The Stationarity Of The
Information While The Johansen Methodology Was Utilized To Test For The Quantity Of Co-Combination
Vectors In The Model. Johansen Procedure Was Utilized Not Just On The Grounds That It Is Vector Auto-
Backward Based But Since It Performs Better In Multivariate Model. In The Event That Xt And Yt Are Then
Co-Coordinated, Their Short-Run Flow Can Be Depicted By Error Correction Model (ECM). The Hypothesis
Expresses That If Two Factors Y And X Is Co-Coordinated, At That Point The Connection Between Them Can
Be Communicated As ECM.
IV. Results And Discussions
Unit Root Testing
The Table 1 Presents The ADF Unit Root Tests For Each Of The Variables. All The Variables, Except Carbon
Dioxide Are Stationary At Level. Carbon Dioxide Was However Stationary After First Differencing.
Augmented Dickey Fuller Test
Tabl1 1: Stationarity Results From The Augmented Dickey Fuller Test
Level 1(0)
1stDifference 1(1)
Variable
Test
Statistic
P-Value
Decision
Test Statistic
P-Value
Decision
Maize Output (Lny)
-4.645
0.0009*
Stationary
-
-
-
Rainfall (Lnx1)
-6.449
0.0000*
Stationary
-
-
-
Rainfall Squared
(Lnx2)
-6.639
0.0000*
Stationary
-
-
-
Effect of Climate Change on Maize Productivity in Kenya:A Vector Error Correction Model
DOI: 10.9790/5933-0902012833 www.iosrjournals.org 31 | Page
Temperature (Lnx3)
-6.067
0.0000*
Stationary
-
-
-
Temperature Squared
(Lnx4)
-6.012
0.0000*
Stationary
-
-
-
Carbon Dioxide
(Lnx5)
-2.061
0.5679
Unit Root
-7.525
0.0000*
Stationary
Carbon Dioxide
Squared (Lnx6)
-1.380
0.8668
Unit Root
-8.193
0.0000*
Stationary
Source:Authors‟ Computation From STATA Software, 2017
*, Denotes Statistical Significance At The 5 Percent Significance Level. The Critical Values For The 52
Observations: ADF Statistics -4.146, -3.498 And -3.179
Table 2 Presents The Johansen Co-Integration Result. The Likelihood Ratio Shows That There Are
Three Co-Integrating (CI) Equations In The Analysis. Only One Of The CI Equations Was Chosen. The CI
Equation Chosen Was Based On The Conformity Of The Coefficients With Economic Theory And Its Statistical
Significance. From The Equation, All The Independent Variables Considered Are Significantly Having Effect
On Maize Production In Kenya During The Study Period.
Table 2: Johansen Cointegration Results
Eigen Value
Log Likelihood Ratio
5%
Hypothesized No Of CE(S)
0.68863
187.68
124.24
None
0.61440
127.00
94.15
None
0.49368
77.45
68.52
None
0.31996
42.06
47.21
At Most 3
0.25125
22.01
29.68
None
0.12525
6.963
15.41
None
0.00009
0.005
3.76
None
Source:Authors‟ Computation From STATA Software, 2017
Log Likelihood Ratio Indicates 3 Cointegrating Equations At 5% Level Of Significance.
Since It Has Been Ascertained That The Variables Exhibit Unit Root I (1) (Non-Stationary) At Their Levels But
Stationary After Differencing And There Exist A Long Run Relationship Between The Variables, Error
Correction Model Is Thus Formulated.
Longrun Relationship Table3: Longrun Relationship
Maize Output
Coefficient
Std. Error
T-Statistic
P-Values
Temperature
.0444879
.021031
2.12
0.040
Temperature Squared
-.0003586
.0001696
-2.11
0.040
Rainfall
-1.644069
6.088272
-0.27
0.788
Rainfall Squared
.0339828
.1233011
0.28
0.784
CO2
.0002144
.0000535
4.00
0.000
CO2 Squared
-9.79e-09
3.34e-09
-2.93
0.005
Constant
32.24394
75.15951
0.43
0.670
F( 6, 46) = 13.32
Prob>F = 0.0000
R-Squared = 0.6347
Adj R-Squared = 0.5870
Root MSE = 0.21054
Source: Author, 2017
Within The Period Under Study, Temperature, Temperature Squared, CO2 And CO2 Squared Were
Found To Be Statistically Significant. Long Run Relationships Indicated That A Unit Increase In Temperature
And CO2 Would Result In A Positive Increase In Maize Output By .0444879 And .0002144 Respectively.
Conversely, Despite Temperature Squared And CO2 Squared Being Statistically Significant, Results Indicated
That A Unit Increase In Their Levels Would Result In A Decrease In Maize Output By A Value Of.0003586
And 9.79e-09 Respectively.
Rainfall And Rainfall Squared Was Found To Be Negatively And Positively Insignificant Respectively
In Relation To Maize Production( P-Value 0.788, 0.784 > 0.05)
Table 4: Results From The Error Correction Model
Dependent Variable: Maize Output
Variable
Coefficient
Standard Error
P>/Z/
Rainfall (Lnx1)
-0.972
0.131
0.000
Rainfall Squared (Lnx2)
0.008
0.001
0.000
Temperature (Lnx3)
97.6
38.61
0.011
Temperature Squared (Lnx4)
-1.91
0.782
0.014
Carbon Dioxide (Lnx5)
-0.001
0.000
0.002
Carbon Dioxide Squared (Lnx6)
4.73
2.23
0.034
Effect of Climate Change on Maize Productivity in Kenya:A Vector Error Correction Model
DOI: 10.9790/5933-0902012833 www.iosrjournals.org 32 | Page
Source:Authors‟ Computation From STATA Software, 2017
Table 3 Presents The Short Run Relationships After Normalization Between Maize Output And The
Various Independent Variables Using Equation 2. From The Results, Rainfall Squared, Temperature And
Carbon Dioxide Squared Had A Positive (Direct) And Significant Effect On Maize Output Respectively (P-
Value 0.000, 0.011, 0,034< 0,05). Rainfall, Temperature Squared And Carbon Dioxide Had A Negative
(Indirect) Significant Effect On Maize Output Respectively (P-Value 0.000, 0.014, 0.002< 0.05).
Table 3 Also Indicates That A Unit Increase In Rainfall Results In A Decrease In Maize Output By
972. This Is Different For Rainfall Squared Whose Coefficient Show That A Unit Increase In Rainfall Squared
Results In An Increase In Maize Productivity By 0.008. This Is In The Same Direction With Temperature
Where A Unit Increase In Temperature Results In An Increase In Maize Output By 97.6. Conversely, A Unit
Change In Temperature Squared Results In Decrease In Maize Productivity By 1.91. This Also Applies To
Carbon Dioxide Whereby A Unit Change In The Levels Of Carbon Dioxide Results In A Decrease In Maize
Productivity By 0.001. Carbon Dioxide Squared Exhibited A Positive Coefficient Whereby A Unit Change In
Carbon Dioxide Squared Resulted In An Increase In Maize Productivity By 4.73.
V. Model Appropriateness
Test For Serial Correlation
Lags(P)
Chi2
Df
Prob> Chi2
1
1.152
1
0.2831
H0: No Serial Correlation
Guided By The Null Hypothesis Of No Serial Correlation, Breusch-Godfrey LM Test For Autocorrelation
Indicated A Probability Of 0.2831 Which Was Greater Than 0.05 Hence Accepting The Null Hypothesis Of No
Serial Correlation.
Test For Heteroskedasticity
Breusch- Pagans, Cook=Weisberg Test For Heteroscedasticity Indicated A Probability 0f 0.0675
Which Was Greater Than The Standard 0.05 Hence Leading To The Acceptance Of The Null Hypothesis Of
Constant Variance Hence No Heteroscedasticity
Lags(P)
Chi2
Df
Prob>Chi2
1
3.34
1
0.0675
Ho: Constant Variance
VI. Conclusion And Policy Recommendation
This Study Investigated The Effects Of Climate Change On Maize Productivity In Kenya Between 1961 And
2013. Results From The Investigation Revealed That For Sure The Rampant Volatility In Maize Productivity Is
Due To Many Factors Related To Temperature, Carbon Dioxide Emissions, And Rainfall Among Many Factors
Relating To The Above. It Is Therefore Recommended That Government Should Be In A Position To Monitor
Activities That May Affect The Listed Factors To Ensure That Maize Productivity Is Constant Or
Improving.The Government Should Seriously Use The Metrological Departments To Monitor The Key
Indicators Of The Climate So As To Advice The Stakeholders In The Maize Subsector Accordingly.
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Wilfreyvuhya Siahi"Effect of Climate Change on Maize Productivity in Kenya:A Vector Error
Correction Model" IOSR Journal of Economics and Finance (IOSR-JEF) , vol. 9, no. 2, 2018,
pp. 28-33.
